Many smart building applications require indoor identification of individuals for personalized tracking and monitoring services. For example, in a nursing home, identifying monitored patients and tracking individual activity range helps nurses understand the condition of patients. Similarly, such identification information can also be used in smart stores/malls to analyze shopping patterns of customers.
Various methods and apparatuses have been explored for identification of individuals. These methods and apparatuses utilize biometrics (face, iris, fingerprints, hand geometry, gait, etc.) and sensing technologies (vision, sound, force, etc.). Some biometrics, such as iris, fingerprints and hand geometry achieve relatively high identification accuracy and are widely used for access control. However, they often require human interactions, and, as such, they have limited usefulness for ubiquitous smart building applications. With other methods, such as facial and gait recognition, it is often difficult to get enough sensing resolution required for recognition from a distance, particularly when used in surveillance applications. Numerous sensing technologies have been explored and proven useful and efficient, but all have limitations. Vision-based methods often require line-of-sight, with performance dependent upon lighting conditions, and may require high computational costs, which limits their viability. Likewise, sound-based methods have limitations when deployed in conversation sensitive areas, as they are prone to be affected by ambient audio. Force-based methods typically utilize specialized floor tile sensors for footstep detection, resulting is the requirement for dense deployment at a high installation cost.